Macro-level Search Space. Most NAS methods follow a fixed macro skeleton with a searched cell. However, the macro-level structure of the backbone can be crucial for the final performance.
Early-stage feature maps in a backbone have larger sizes as they capture texture details, whereas feature maps at later stages are smaller and usually are more discriminative [1]}.
The allocation of computations over different stages is also vital for a backbone [2]}.
Therefore, our search space contains networks with different depth (the total number of blocks), locations to down-sample feature maps, and locations to raise the channels.
As is illustrated in Figure REF , we group two residual blocks [3]} into a module, and the networks are stacked with 4 to 6 modules.
The module positions can be chosen to downsample the feature map 1 to 4 times, and each time the spatial size will shrink by a factor of 2.
The network can double its channels 1 to 3 times at chosen locations. This search space thus consists of 3,256 unique architectures.
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